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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Pan, Jeng-Shyang Han, Kunpeng Chu, Shu-Chuan Li, Zhi Zhang, Li College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Artificial Intelligence Nanjing University of Information Science and Engineering Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan School of Information and Control Engineering Qingdao University of Technology Qingdao266590 China

ISBN: (纸本)9789819615346

In this paper, an improved Gannet optimization algorithm (DDPSA-GOA) is presented. The Gannet optimization algorithm (GOA) was formed by mathematically studying the various foraging behaviors of Gannet. GOA has had better results in the past. A framework DDPSA to improve GOA is cited in this paper. DDPSA aims to adjust population size and replace stagnant individuals with different strategies by determining the stage of the population. The framework divides the population into two stages, premature integration and evolutionary stagnation, which are two population states with stagnant individual replacement mechanisms. Optimal elimination replacement or elite replacement is adopted for different stages. The GOA algorithm in the DDPSA framework was tested against the initial GOA and other comparative algorithms in the CEC 2013 test set. Experimental results show that DDPSA-GOA outperforms other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

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computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

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Improved LSSVM to Predict the Elongation of Strip Steel in Annealing Furnace

Improved LSSVM to Predict the Elongation of Strip Steel in A...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Lv, ChunPeng Shi, YuanBo Huang, YueYang Wang, JianHui Liaoning Petrochemical University College of Computer and Communication Engineering Fushun113001 China Liaoning Petrochemical University College of Information and Control Engineering Fushun113001 China Northeasten University College of Information Science and Engineering Shenyang110819 China

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

It is difficult to predict the elongation of strip steel in annealing furnace due to the influence of temperature, tension, roll speed and data noise. Thus, a strip elongation prediction method based on least squares support vector machine (LSSVM) optimized by artificial bee colony (ABC) algorithm is proposed. In order to improve the convergence speed and accuracy of the algorithm, the new adaptive step update formula, adaptive probability selection formula and global search factor were introduced to improve the standard artificial bee colony algorithm. The parameter of the LSSVM were optimized by improved artificial bee colony(IABC) algorithm which overcame the subjectivity of human selection and made the LSSVM get better generalization and prediction accuracy. Numerical simulation results of MATLAB show that the relative error and root mean square error predicted by IABC-LSSVM are better than those predicted by ABC-LSSVM and LSSVM which effectively improves the convergence speed and prediction accuracy of the algorithm. Under the real working conditions, IABC-LSSVM provides theoretical support for the prediction of strip extension in annealing furnace and has a certain engineering application value. © 2022 IEEE.

关键词： Elongation

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Combination of facial and nose features of Amur tigers to determine age

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Integrative Zoology 2025年第1期20卷 186-198页

作者： Caiping ZHAO Wenrui DAI Qiang LIU Dongqi LIU Nathan James ROBERTS Zhaoli LIU Ming GONG Hongkun QIU Changhai LIU Dan LIU Guangkai MA Guangshun JIANG Feline Research Center of National Forestry and Grassland Administration College of Wildlife and Protected AreaNortheast Forestry UniversityHarbinP.R.China Research Center for Northeast Asia Biodiversity Conservation HarbinP.R.China College of Computer and Control Engineering Northeast Forestry UniversityHarbinP.R.China Heilongjiang Siberia Tiger Park HarbinHeilongjiangP.R.China

INTRODUCTION For a given population,only when the individual age is known can we understand the population age structure,evaluate the population stability,speculate on the future development trend of the population,and formulate reasonable animal management approaches(Coulson et al.2001).The Amur tiger(Panthera tigris altaica)is the largest subspecies of tiger and is mainly distributed in the southeast of Russia at present,while there are also a small number of individuals living in the border region of China,Russia,and North Korea in the northeast of China(Qi et al.2020).Age determination of Amur tigers is required for conservation strategy design,prioritization,and allocation of resources,as well as for evaluating the success of conservation *** is very difficult to determine the age of tiger individuals in the wild.

关键词： Panthera southeast northeast

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Optimal Time and Power Allocation in Full-Duplex Wireless-Powered Cognitive Radio Networks 13

Optimal Time and Power Allocation in Full-Duplex Wireless-Po...

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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2023

作者： Feng, Zhenyu Su, Ming Zhou, Meihua Liu, Zhixin School of Electrical Engineering Yanshan University Qinhuangdao066004 China Training Center of Liren College Yanshan University China Key Lab of Industrial Computer Control Engineering of Hebei Province Hebei Province Qinhuangdao066004 China

ISBN: (纸本)9798350315196

In this paper, we integrate wireless powered communication networks (WPCNs) with cognitive radio networks (CRNs) in order to develop wireless powered CRNs (WP-CRNs). The hybrid access point (HAP) is used to broadcast radio-frequency signals to the devices at the downlink stage, and to receive information from these devices. The HAP has two antennas to achieve full-duplex: one is for downlink wireless power transfer and the other is for receiving uplink information transmission from the users simultaneously in the uplink. In the underlay paradigms. The SUs can harvest energy to transmit data when the interference to primary users (PUs) does not exceed a tolerable threshold. To tackle the above network issues, an optimal resource allocation method is proposed to maximize the secondary system uplink throughput. The optimization problem with respect to time and power allocation is formulated including the constraint of energy causality and interference level. The non-convex problem is transformed into an equivalent convex optimization problem, then the joint optimal power and time allocation (JOPTA) algorithm is presented to determine the optimal solutions. The effectiveness of the proposed algorithm is verified in the simulations. © 2023 IEEE.

关键词： Cognitive radio

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Energy Management Strategy for Fuel Cell Hybrid Emergency Power System based on Deep Reinforcement Learning 4

Energy Management Strategy for Fuel Cell Hybrid Emergency Po...

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4th International Symposium on AI, ISAI 2024

作者： Xi, Jingyi Hu, Xiaoyin Xiao, Duo Zhejiang University College of Control Science and Engineering Hangzhou China Hangzhou City University School of Information and Electrical Engineering Hangzhou China Hangzhou City University School of Computer and Computing Science Hangzhou China Hangzhou City University The Academy of Edge Intelligence Hangzhou China

ISBN: (纸本)9798350342482

Addressing the challenges in energy consumption and lifespan of fuel cell hybrid emergency power system, this paper proposes an energy management strategy based on deep reinforcement learning. The strategy aims to holistically balance objectives such as hydrogen consumption of the fuel cell, its lifespan, the stability of the lithium battery's state of charge (SOC), and its degradation. Firstly, we present the system architecture of the fuel cell hybrid emergency power system and establish models for hydrogen consumption and degradation of the fuel cell, as well as the SOC variations and degradation of the lithium battery, upon which a multi-objective reward function is constructed. Subsequently, we adopt the distributional soft actor-critic (DSAC) deep reinforcement learning algorithm, combined with a maximum entropy-based objective function to balance the exploration-exploitation trade-off and incorporated with a distributed value function estimation to alleviate the overestimation of Q-values. Simulation results demonstrate that the proposed strategy can effectively reduce the overall value loss, extend the system's lifespan, and showcase robust adaptability. © 2024 IEEE.

关键词： Deep reinforcement learning

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Task Offloading Optimization for Mobile Edge Computing System: A CNO Approach 23

Task Offloading Optimization for Mobile Edge Computing Syste...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Long, Changqing Meng, Wenchao Zhang, Yongmin College of Control Science and Engineering Zhejiang University Hangzhou310027 China The Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China School of Computer and Engineering Central South University Changsha410083 China

ISBN: (纸本)9798350325959

Mobile edge computing (MEC) can extend computing power at the edge of wireless networks by migrating compute-intensive tasks to MEC servers, which has the potential to enable compute-intensive applications in 5G networks. However, whether to offload tasks and how much edge computing resources to allocate to it is an NP-hard problem. In this paper, a MEC system enabled multi-user wireless network is proposed, in which the access point (AP) is equipped with a MEC server that provides computing resources for mobile users through task offloading. Then, the issue of task offloading and resource allocation is formulated as a mixed integer nonlinear program to minimize the overhead of the entire MEC system, represented by a weighted sum of delay and energy consumption. Furthermore, a collaborative neurodynamic optimization (CNO) with levy mutation operator (CNO-LMO) algorithm is proposed for searching global optimal solutions to the formulated problem. Finally, the experimental results are given to verify the superiority of the proposed CNO-LMO in reducing the overhead of the MEC system compared to some other algorithms. © 2023 IEEE.

关键词： collaborative neurodynamic optimization (CNO) mixed integer nonlinear program (MINLP) mobile edge computing (MEC) resource allocation task offloading

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Optimizing Steering Angle Predictive Convolutional Neural Network for Autonomous Car

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computers, Materials & Continua 2022年第5期71卷 2285-2302页

作者： Hajira Saleem Faisal Riaz Asadullah Shaikh Khairan Rajab Adel Rajab Muhammad Akram Mana Saleh Al Reshan Control Automotive and Robotics Lab Affiliated lab of National Center of Robotics and Automation(NCRA HEC Pakistan)and with the Department of Computer Science and Information TechnologyMirpur University of Science and Technology(MUST)Mirpur Azad Kashmir10250Pakistan College of Computer Science and Information System Najran UniversityNajran61441Saudi Arabia College of Computer Science and Engineering University of South FloridaTampa33620United States

Deep learning techniques,particularly convolutional neural networks(CNNs),have exhibited remarkable performance in solving visionrelated problems,especially in unpredictable,dynamic,and challenging *** autonomous vehicles,imitation-learning-based steering angle prediction is viable due to the visual imagery comprehension of *** this regard,globally,researchers are currently focusing on the architectural design and optimization of the hyperparameters of CNNs to achieve the best *** has proven the superiority of metaheuristic algorithms over the manual-tuning of ***,to the best of our knowledge,these techniques are yet to be applied to address the problem of imitationlearning-based steering angle ***,in this study,we examine the application of the bat algorithm and particle swarm optimization algorithm for the optimization of the CNN model and its hyperparameters,which are employed to solve the steering angle prediction *** validate the performance of each hyperparameters’set and architectural parameters’set,we utilized the Udacity steering angle dataset and obtained the best results at the following hyperparameter set:optimizer,Adagrad;learning rate,0.0052;and nonlinear activation function,exponential linear *** per our findings,we determined that the deep learning models show better results but require more training epochs and time as compared to shallower *** show the superiority of our approach in optimizing CNNs through metaheuristic algorithms as compared with the manual-tuning *** testing was also performed using the model trained with the optimal architecture,which we developed using our approach.

关键词： Bat algorithm convolutional neural network hyperparameters metaheuristic optimization algorithm steering angle prediction

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Retinal Vessel Segmentation Based on UNet and Edge Detection 12

Retinal Vessel Segmentation Based on UNet and Edge Detection

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12th International Conference on Information Technology in Medicine and Education, ITME 2022

作者： Yan, Peixin Li, Zuoyong Hu, Rong Cao, Xinrong Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Colloge of Computer and Control Engineering Fuzhou350121 China

ISBN: (纸本)9798350310153

Retinal vessel segmentation is important for the diagnosis of many diseases. The segmentation of microscopic vessels is one of the challenges because of the complex structure of retinal vessels, which makes it challenging to tell the vessels apart from the background. In order to improve the segmentation performance of retinal vessels, we use the edge detection method of HED to optimize the segmentation effect of UNet. Using Hybrid Dilated Convolution not only increases the perceptual field, but also avoids the loss of information continuity caused by the continuous use of dilated convolution. The Pyramid Pooling Module is used at the end of the encoder to compensate for the information loss caused by downsampling. Finally, the output of edge detection and image segmentation is combined as the final result. The model is evaluated in two mainstream datasets DRIVE and CHASE-DB1, and the experimental results show that the proposed model can improve the performance of retinal vessel segmentation. © 2022 IEEE.

关键词： Convolution

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TC-Diffuser: Bi-Condition Multi-Modal Diffusion for Tropical Cyclone Forecasting 39

TC-Diffuser: Bi-Condition Multi-Modal Diffusion for Tropical...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Shiqi Mu, Pan Huang, Cheng Zhang, Jinglin Bai, Cong College of Computer Science Zhejiang University of Technology China School of Control Science and Engineering Shangdong University China

ISBN: (纸本)157735897X

Tropical cyclones (TCs) are complex weather systems with strong winds and heavy rainfall, causing substantial loss of life and property. Therefore, accurate TC forecasting is crucial for the effective prevention of disasters caused by TCs. TC forecasting can be regarded as a spatio-temporal prediction problem. It has been proven that using multi-modal data can effectively introduce atmospheric information to achieve better prediction results and higher interpretability. But it also introduces inevitably introduces noise into the prediction process. The diffusion model’s unique noise modeling capability can reduce prediction noise when using multi-modal datasets. However, adapting it to TC forecasting has two main challenges: how to extract valuable information from multi-modal data, and how to utilize them to guide the generation process. For the first challenge, while recent methods can predict multiple TC attributes using multi-modal data, they often overlook the interdependence of multiple attributes and the semantic gap between modalities. Considering the interdependence of attributes, we propose two condition generators that capture the commonalities and characteristics of TC attributes, extracting spatio-temporal and environmental features and incorporating expert knowledge. To reduce the semantic gap between multi-modal data, we introduce the PGSA-LSTM module to map primary and auxiliary modalities. For the second challenge, we propose a novel Bi-condition diffusion model that sequentially processes conditions from the characteristics to commonalities of attributes, thereby expanding the guidance information that the diffusion model can accept. Our results surpass state-of-the-art deep learning models and outperform the numerical weather prediction model used by the China Central Meteorological Observatory. TC-Diffuser shows high generalizability across global ocean areas, strong robustness in handling missing data, and higher computational efficiency. © 2025

关键词： Tropical cyclone

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